Dr Vivek Singh
Profiles

Dr Vivek Singh

Lecturer in Computer Science

School of Engineering, Computing and Mathematics (Faculty of Science and Engineering)

Biography

Biography

Vivek Singh is Lecturer in the School of Engineering, Computing and Mathematics at the University of Plymouth. His research and teaching activities are centered around Artificial Intelligence and Computer Vision. He has extensive experience in tackling AI related problems ranging from medical image analysis to cutting-edge computer vision applications. 

His research interest mainly focuses on: Artificial Intelligence, Computer Vision, Data-efficient learning methods, Multi-modal learning, Scene understanding, Medical Image Analysis

Qualifications

Before University of Plymouth, he worked for Supponor and was involved in the development of virtual advertisement solution for live sports broadcasts. Before that, he worked on Smart Autonomous Robotic Assistant Surgeon as a PostDoc Research Fellow at Oxford Brookes University. He completed his doctorate from Thapar Institute in computer vision and representation learning.
Teaching

Teaching

Teaching interests

COMP5013                Topics in Applied Artificial Intelligence
WBL203                     Systems Design and Development
Research

Research

Research interests

  • Artificial Intelligence (AI)
  • Computer Vision
  • Data-efficient learning methods
  • Multi-modal learning
  • Scene understanding
  • Medical Image Analsysis
Publications

Publications

Journals
  • Bawa, V. S., Sharma, S., Usman, M., Gupta, A., & Kumar, V. (2021). An Automatic Multimedia Likability Prediction System Based on Facial Expression of Observer. Ieee Access, 9, 110421-110434.
  • Bawa, V. S., & Kumar, V. (2020). Mutually independent feature flow: An approach to produce fixed complexity latent space for improved performance and decreased parameter count. Future Generation Computer Systems, 110, 1067-1078.
  • Bawa, V. S., & Kumar, V. (2019). Linearized sigmoidal activation: A novel activation function with tractable non-linear characteristics to boost representation capability. Expert Systems with Applications, 120, 346-356.
  • Bawa, V. S., & Kumar, V. (2019). Emotional sentiment analysis for a group of people based on transfer learning with a multi-modal system. Neural Computing and Applications, 31, 9061-9072.
Chapters
  • Rast, A., Singh, V., Plunkett, S., Crean, A., & Cuzzolin, F. (2023). What Are We Automating? On the Need for Vision and Expertise When Deploying AI Systems. In Business Digital Transformation: Selected Cases from Industry Leaders (pp. 17-43). Cham: Springer International Publishing.
Conference Papers
  • Sharma, S., Dhall, A., Kumar, D. V., & Singh, V. (2023). Dual Stage Semantic Information Based Generative Adversarial Network For Image Super-Resolution. In Proceedings of the Fourteenth Indian Conference on Computer Vision, Graphics and Image Processing (pp. 1-9).
  • Teeti, I., Bhargav, R. S., Singh, V., Bradley, A., Banerjee, B., & Cuzzolin, F. (2023, October). Temporal DINO: A Self-supervised Video Strategy to Enhance Action Prediction. In 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) (pp. 3273-3283). IEEE.
  • Eisenmann, M., Reinke, A., Weru, V., Tizabi, M. D., Isensee, F., Adler, T. J., ... & Maier-Hein, L. (2023). Why is the winner the best?. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 19955-19966).